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--- |
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license: apache-2.0 |
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language: |
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- fr |
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multilinguality: |
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- monolingual |
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tags: |
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- finetuning |
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- legal |
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- french law |
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- droit français |
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- Code général des impôts, annexe III |
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source_datasets: |
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- original |
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pretty_name: Code général des impôts, annexe III |
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task_categories: |
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- text-generation |
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- table-question-answering |
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- summarization |
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- text-retrieval |
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- question-answering |
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- text-classification |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Code général des impôts, annexe III, non-instruct (2024-04-01) |
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This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice. |
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Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach. |
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Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks. |
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Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways: |
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- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions. |
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- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs. |
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- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more. |
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- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs. |
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- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text. |
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## Concurrent reading of the LegalKit |
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To use all the legal data published on LegalKit, you can use this code snippet: |
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```python |
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# -*- coding: utf-8 -*- |
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import concurrent.futures |
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import os |
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import datasets |
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from tqdm.notebook import tqdm |
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def dataset_loader( |
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name:str, |
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streaming:bool=True |
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) -> datasets.Dataset: |
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""" |
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Helper function to load a single dataset in parallel. |
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Parameters |
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---------- |
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name : str |
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Name of the dataset to be loaded. |
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streaming : bool, optional |
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Determines if datasets are streamed. Default is True. |
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Returns |
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------- |
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dataset : datasets.Dataset |
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Loaded dataset object. |
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Raises |
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------ |
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Exception |
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If an error occurs during dataset loading. |
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""" |
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try: |
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return datasets.load_dataset( |
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name, |
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split="train", |
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streaming=streaming |
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) |
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except Exception as exc: |
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logging.error(f"Error loading dataset {name}: {exc}") |
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return None |
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def load_datasets( |
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req:list, |
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streaming:bool=True |
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) -> list: |
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""" |
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Downloads datasets specified in a list and creates a list of loaded datasets. |
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Parameters |
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---------- |
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req : list |
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A list containing the names of datasets to be downloaded. |
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streaming : bool, optional |
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Determines if datasets are streamed. Default is True. |
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Returns |
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------- |
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datasets_list : list |
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A list containing loaded datasets as per the requested names provided in 'req'. |
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Raises |
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------ |
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Exception |
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If an error occurs during dataset loading or processing. |
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Examples |
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-------- |
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>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False) |
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""" |
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datasets_list = [] |
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with concurrent.futures.ThreadPoolExecutor() as executor: |
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future_to_dataset = {executor.submit(dataset_loader, name): name for name in req} |
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for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)): |
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name = future_to_dataset[future] |
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try: |
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dataset = future.result() |
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if dataset: |
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datasets_list.append(dataset) |
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except Exception as exc: |
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logging.error(f"Error processing dataset {name}: {exc}") |
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return datasets_list |
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req = [ |
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"louisbrulenaudet/code-artisanat", |
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"louisbrulenaudet/code-action-sociale-familles", |
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# ... |
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] |
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datasets_list = load_datasets( |
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req=req, |
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streaming=True |
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) |
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dataset = datasets.concatenate_datasets( |
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datasets_list |
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) |
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``` |
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## Dataset generation |
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This JSON file is a list of dictionaries, each dictionary contains the following fields: |
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- `instruction`: `string`, presenting the instruction linked to the element. |
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- `input`: `string`, signifying the input details for the element. |
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- `output`: `string`, indicating the output information for the element. |
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- `start`: `string`, the date of entry into force of the article. |
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- `expiration`: `string`, the date of expiration of the article. |
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- `num`: `string`, the id of the article. |
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We used the following list of instructions for generating the dataset: |
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```python |
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instructions = [ |
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"Compose l'intégralité de l'article sous forme écrite.", |
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"Écris la totalité du contenu de l'article.", |
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"Formule la totalité du texte présent dans l'article.", |
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"Produis l'intégralité de l'article en écriture.", |
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"Développe l'article dans son ensemble par écrit.", |
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"Génère l'ensemble du texte contenu dans l'article.", |
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"Formule le contenu intégral de l'article en entier.", |
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"Rédige la totalité du texte de l'article en entier.", |
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"Compose l'intégralité du contenu textuel de l'article.", |
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"Rédige l'ensemble du texte qui constitue l'article.", |
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"Formule l'article entier dans son contenu écrit.", |
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"Composez l'intégralité de l'article sous forme écrite.", |
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"Écrivez la totalité du contenu de l'article.", |
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"Formulez la totalité du texte présent dans l'article.", |
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"Développez l'article dans son ensemble par écrit.", |
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"Générez l'ensemble du texte contenu dans l'article.", |
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"Formulez le contenu intégral de l'article en entier.", |
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"Rédigez la totalité du texte de l'article en entier.", |
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"Composez l'intégralité du contenu textuel de l'article.", |
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"Écrivez l'article dans son intégralité en termes de texte.", |
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"Rédigez l'ensemble du texte qui constitue l'article.", |
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"Formulez l'article entier dans son contenu écrit.", |
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"Composer l'intégralité de l'article sous forme écrite.", |
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"Écrire la totalité du contenu de l'article.", |
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"Formuler la totalité du texte présent dans l'article.", |
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"Produire l'intégralité de l'article en écriture.", |
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"Développer l'article dans son ensemble par écrit.", |
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"Générer l'ensemble du texte contenu dans l'article.", |
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"Formuler le contenu intégral de l'article en entier.", |
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"Rédiger la totalité du texte de l'article en entier.", |
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"Composer l'intégralité du contenu textuel de l'article.", |
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"Rédiger l'ensemble du texte qui constitue l'article.", |
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"Formuler l'article entier dans son contenu écrit.", |
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"Quelles sont les dispositions de l'article ?", |
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"Quelles dispositions sont incluses dans l'article ?", |
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"Quelles sont les dispositions énoncées dans l'article ?", |
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"Quel est le texte intégral de l'article ?", |
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"Quelle est la lettre de l'article ?" |
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] |
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``` |
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## Feedback |
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If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
